Computed tomography is an imaging method, which is used above all for medical diagnostics and also for examination of materials. In computed tomography, to record spatially three-dimensional image data, a radiation source, for example an x-ray source, and also a detector facility interacting with said source, rotate around an object to be examined. During the rotational movement measurement data is recorded within an angular sector. The projection measurement data involves a plurality of projections, which contain information about the attenuation of the radiation by the examination object from different projection angles. A two-dimensional slice image or a three-dimensional volume image of the examination object is able to be computed from these projections. The projection measurement data is also referred to as raw data or the projection measurement data can already be pre-processed data, so that for example detector-related intensity differences in the attenuation are reduced. Image data can then be reconstructed from this projection measurement data, by way of what is known as filtered back projection for example, or via an iterative reconstruction method. If the examination object moves during the recording, unsharp areas and artifacts resulting from the movement can occur during the reconstruction of the image data.
Multifarious methods for scanning an examination object with a computed tomography system are known. Orbital scans, sequential orbital scans with advance or spiral scans are employed for example. Other types of scanning, which are not based on orbital movements, are also possible, thus scans with linear segments for example. Absorption data of the examination object is recorded from different recording angles with the aid of at least one x-ray source and at least one opposing detector apparatus and this absorption data or the projections collected in this way are computed into slice images through the examination object via corresponding recording methods.
For reconstruction of computed tomography images from the projection measurement data of a computed tomography system, what is known as a filtered back projection method (FBP) is used as the standard method nowadays. However, because of their approximative method of operation, there are problems with the classical FBP methods with so-called cone beam artifacts, spiral artifacts and limited-view artifacts. Furthermore, with classical FBP methods, the image sharpness is coupled to the image noise. The higher the sharpness achieved is, the higher is also the image noise and vice versa.
The FBP method belongs to the group of approximative reconstruction methods. The group of exact reconstruction methods, which is currently hardly used however, also exists. The iterative methods form a third group of reconstruction methods.
With iterative reconstruction methods at least some of the stated limitations of FBP can be overcome. With such an iterative reconstruction method there is first of all a reconstruction of start image data from the projection measurement data. A filtered back projection method can be used for this purpose for example. The iterative reconstruction method subsequently gradually creates improved image data. Synthetic projection data can be created from the start image data with a “projector”, a projection operator that is designed to map the measurement system mathematically as well as possible for example. The difference from the measurement signals will then be projected back with the operator adjoint to the projector and in this way a residual image reconstructed, with which the initial image will be updated. The updated image data in its turn can be used, in a next iteration step, with the aid of the projection operator, to create new synthetic projection data, to once again form the difference from the measurement signals from this and compute a new residual image, with which the image data of the current iteration stage will again be improved etc. Such a method allows image data that has a relatively good image sharpness and still has a low level of image noise to be reconstructed. Examples of iterative reconstruction methods are the Algebraic Reconstruction Technique (ART), the Simultaneous Algebraic Reconstruction Technique (SART), the Iterated Filtered Back Projection (IFBP), or also statistical iterative image reconstruction techniques.
Counting direct-converting x-ray detectors or integrating indirect-converting x-ray detectors can be used in computed tomography. The x-ray radiation or the photons can be converted in direct-converting x-ray detectors by a suitable converter material into electrical pulses. The level of the electrical pulses is as a rule proportional to the energy of the absorbed x-ray photon. This enables spectral information to be extracted by comparing the level of the electrical pulse with a threshold value. The x-ray radiation of the photons can also be converted into light by a suitable converter material in indirect-converting x-ray detectors and via photodiodes into the electrical pulse. The level of the electrical pulse specifies an integral intensity of the detected x-ray radiation.
In dual-energy computed tomography (Dual Energy CT), dual-source computed tomography (Dual Source CT) as well as with the use of energy-resolving counting x-ray detectors, for example direct-converting x-ray detectors, material decompositions into two or three materials can be carried out on the basis of the measured datasets. In dual-energy computed tomography different spectra can be created for example by switching back and forth between different tube voltages or by the use of a filter partly embodied in the beam path. In dual-source computed tomography the two x-ray sources can be operated with different tube voltages. The inventors have recognized that it is difficult to create more than two spectral datasets with one single x-ray source.
A method for time-resolved computed tomography, which provides a new method for elimination of limited-view artifacts, is known from G. Chen, Y. Li, “Synchronized multiartefact reduction with tomographic reconstruction (SMART-RECON): A statistical model based iterative image reconstruction method to eliminate limited-view artifacts and to mitigate the temporal-average artifacts in time-resolved CT”, Med. Phys. 42, 4698 (2015). In this method data recorded in an ultrashort time window, which corresponds to angular ranges of around 60°, is used.